Uncertainties Analysis and Improvement of Smoothness TES Algorithm Based on Simulation Hyper-Cam-LW Airborne Imagery

In recent years, several commercial airborne thermal infrared (TIR) hyperspectral imagers have been available for remote sensing applications. However, unlike the sensors developed primarily for research and government use, commercial imagers have particular configurations and imaging features, leading to uncertainties of mainstream smoothness temperature and emissivity separation (TES) methods. This letter systematically investigates the feasibility and the performance of retrieving temperature and emissivity from the hyper-Cam-LW sensor using typical smoothness TES algorithms. In addition, the linear spectral emissivity constraint (LSEC) TES algorithm was improved by considering the correlation among atmospheric parameters. Analysis results illustrated that typical smoothness TES algorithms exhibit high sensitivity to noise and flight height. Moreover, the retrieval accuracy of different ground objects varies significantly for the same TES method. By contrast, the improved LSEC-TES algorithm is not susceptible to flight height and the type of ground objects, and its retrieval errors of LST and LSE are less than 0.2 K and 0.004, respectively. Finally, a helpful conclusion can be drawn that the correlation between atmospheric parameters must be considered for the TES of airborne TIR hyperspectral imagery (HSI), and noise must be suppressed to ensure smoothness TES is effective for airborne TIR HSI of commercial TIR hyperspectral imager.